WO2019144710A1

WO2019144710A1 - Method and apparatus for determining position of pupil

Info

Publication number: WO2019144710A1
Application number: PCT/CN2018/119882
Authority: WO
Inventors: 聂凤梅; 刘伟; 任冬淳; 王健; 杨孟; 宫小虎
Original assignee: 北京七鑫易维信息技术有限公司
Priority date: 2018-01-23
Filing date: 2018-12-07
Publication date: 2019-08-01
Also published as: US20200273198A1; TWI714952B; CN108197594B; CN108197594A; US10949991B2; TW201933050A

Abstract

A method and apparatus for determining a position of a pupil. Said method comprises: acquiring an image which includes a pupil and is to be detected (S102); acquiring, on the basis of a predetermined model for semi-supervisory learning, a binary image corresponding to a predetermined region, the predetermined region being a region where the pupil is located in the image to be detected (S104); acquiring a center of mass of the binary image (S106); and determining the center position of the pupil according to the center of mass of the binary image (S108).

Description

Method and apparatus for determining pupil position

The present application claims priority to Chinese Patent Application No. 201810064311.2, entitled "Method and Apparatus for Determining the Position of the Pupil", issued January 23, 2018, the entire contents of which is incorporated herein by reference. in.

Technical field

Embodiments of the present invention relate to the field of image processing, and in particular, to a method and apparatus for determining a pupil position.

Background technique

Virtual Reality (VR) technology is a computer technology that can create and experience virtual worlds. It has been widely used in the field of line-of-sight tracking.

In practical applications, the VR device can perform line-of-sight estimation of the distant device of the fixation point according to the pupil center coordinates and corneal reflection in the eyeball-based 3D approximate sphere model. At present, when positioning the pupil center, the unsupervised learning method is adopted, that is, the model is trained using the unlabeled data. However, the method can only roughly determine the position of the pupil center, and the accuracy is poor.

In view of the above problem that the related art cannot accurately position the center position of the pupil, an effective solution has not been proposed yet.

Summary of the invention

Embodiments of the present invention provide a method and apparatus for determining a pupil position to at least solve the technical problem that the prior art cannot accurately position the pupil center.

According to an aspect of the embodiments of the present invention, a method for determining a position of a pupil includes: acquiring an image to be detected including a pupil; acquiring a binary image corresponding to the preset region based on a preset model of semi-supervised learning, wherein, Let the area be the area where the pupil is located in the image to be detected; obtain the centroid of the binary image; determine the center position of the pupil according to the centroid of the binary image.

According to another aspect of the embodiments of the present invention, there is further provided an apparatus for determining a position of a pupil, comprising: a first acquisition module configured to acquire an image to be detected including a pupil; and a second acquisition module configured to be based on semi-supervised learning The preset model acquires a binary image corresponding to the preset area, wherein the preset area is an area where the pupil is located in the image to be detected; the third acquiring module is configured to acquire a centroid of the binary image; and the determining module is set to be based on the binary value The center of mass of the image determines the center of the pupil.

According to another aspect of an embodiment of the present invention, there is also provided a storage medium comprising a stored program, wherein the program performs a method of determining a pupil position.

According to another aspect of an embodiment of the present invention, there is also provided a processor for running a program, wherein a method of determining a pupil position is performed while the program is running.

In the embodiment of the present invention, the image to be detected including the pupil is obtained by using a semi-supervised learning algorithm, and then the binary image corresponding to the preset region and the centroid of the binary image are acquired based on the preset model of semi-supervised learning. And determining the center position of the pupil according to the centroid of the binary image, wherein the preset area is the area where the pupil is located in the image to be detected, and the purpose of positioning the center of the pupil is achieved. Since semi-supervised learning includes two unsupervised learning and supervised learning processes, the pre-set model obtained by combining supervised learning with unsupervised learning avoids the use of unsupervised learning or only supervised learning. Precise positioning of the pupil. In addition, using the preset model, the image to be detected containing the pupil is converted into a binary image with relatively simple processing, and the position of the pupil center can be accurately determined according to the centroid of the binary image, thereby realizing the accurate determination of the position of the pupil center. The technical effect further solves the technical problem that the prior art cannot accurately position the pupil center.

DRAWINGS

The drawings are intended to provide a further understanding of the embodiments of the present invention, and are intended to be a part of the present invention, and the description of the present invention is not intended to limit the invention. In the drawing:

1 is a flow chart of a method of determining a pupil position according to an embodiment of the present invention;

2 is a schematic structural diagram of an optional binary image according to an embodiment of the present invention;

3(a) is a schematic diagram of an optional labelless training set in accordance with an embodiment of the present invention;

3(b) is a schematic diagram of an optional tagged training set in accordance with an embodiment of the present invention;

4 is a flow chart showing the construction of an optional preset model according to an embodiment of the present invention;

FIG. 5 is a schematic structural view of an apparatus for determining a pupil position according to an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is an embodiment of the invention, but not all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of the present invention.

It is to be understood that the terms "first", "second" and the like in the specification and claims of the present invention are used to distinguish similar objects, and are not necessarily used to describe a particular order or order. It is to be understood that the data so used may be interchanged where appropriate, so that the embodiments of the invention described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "comprises" and "comprises" and "the" and "the" are intended to cover a non-exclusive inclusion, for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to Those steps or units may include other steps or units not explicitly listed or inherent to such processes, methods, products or devices.

Example 1

In accordance with an embodiment of the present invention, an embodiment of a method of determining a pupil position is provided, and it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and Although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in a different order than the ones described herein.

1 is a flow chart of a method for determining a pupil position according to an embodiment of the present invention. As shown in FIG. 1, the method includes the following steps:

Step S102: Acquire an image to be detected including a pupil.

It should be noted that the image capturing device can collect an image including the pupil, that is, obtain the image to be detected. The image to be detected may be one piece or multiple sheets. In the case that there are multiple images to be detected, the image acquisition device collects a set of images to be detected including the pupil. In addition, after the image acquisition device collects the image to be detected, the processor connected to the image acquisition device may process the image to be detected to determine the center position of the pupil in the image to be detected.

Step S104: Acquire a binary image corresponding to the preset area based on the preset model of semi-supervised learning.

The preset area of this embodiment is the area where the pupil is located in the image to be detected.

It should be noted that semi-supervised learning is a machine learning method that combines supervised learning with unsupervised learning. Semi-supervised learning is used to train the preset model, which not only simplifies the model, but also obtains precision comparison. High processing results. In addition, after obtaining the image to be detected, the processor uses the image to be detected as an input of the preset model to process the detected image, and the output of the corresponding preset model is a binary image of the region where the pupil is located in the image to be detected.

In addition, it should be noted that the binary image refers to an image in which only two possible values or gray levels are available for each pixel. Since the binary image has the advantages of less memory, high contrast, and the like, in the present application, the image to be detected including multiple gray levels or multiple colors is processed into a binary image with less value and less gray level. Increase the precision of the pupil center, and also improve the speed of data processing.

In addition, if the input of the preset model is the image set to be detected, the output of the preset model is a binary image set including a plurality of binary images, wherein each image in the binary image set is concentrated with the image to be detected. The image corresponds.

Step S106, acquiring a centroid of the binary image.

It should be noted that, after obtaining the binary image of the image to be detected, the coordinates of the pixel points in the pupil region in the binary image are obtained, and then weighted and summed according to the coordinates of the pixel points of the pupil region, that is, the binary image is obtained. Centroid.

In an alternative embodiment, a schematic diagram of an alternative binary image as shown in FIG. Here, in FIG. 2, a black circle indicates a pupil area in the binary image area. Since it is a binary image, it is only necessary to find the coordinates of the pixel points with the gradation level of 0 in the binary image, and the coordinates of the pixel points in the pupil region can be obtained, and the pupil in the binary image can be obtained by the following formula. The center of mass:

In the above formula, M represents the total number of pixel points in the pupil area, i represents the subscript of the pixel point in the pupil area, x _i , y _i represents the coordinates of the i-th pixel point in the pupil area, and x and y represent the centroid coordinate.

In step S108, the center position of the pupil is determined according to the centroid of the binary image.

It should be noted that after obtaining the centroid of the binary image, the centroid of the binary image is the center position of the pupil.

Based on the solution defined in the foregoing steps S102 to S108, it can be known that the image to be detected including the pupil is obtained, and then the binary image corresponding to the preset region and the centroid of the binary image are acquired based on the preset model of semi-supervised learning. And determining the center position of the pupil according to the centroid of the binary image, wherein the preset area is the area where the pupil is located in the image to be detected.

It is easy to notice that since semi-supervised learning includes two learning processes: unsupervised learning and supervised learning, the pre-set model obtained by combining supervised learning and unsupervised learning can overcome the use of only relevant technologies. Supervised learning or using only supervised learning can not accurately locate the pupil. In addition, using the preset model, the image to be detected containing the pupil is converted into a binary image with relatively simple processing, and the position of the pupil center can be accurately determined according to the centroid of the binary image. In addition, the above calculation process is simple, and the speed of precise positioning of the pupil center is improved.

It can be seen from the above that the embodiment provided by the present application can achieve the purpose of positioning the pupil center, thereby realizing the technical effect of accurately determining the position of the pupil center, and further solving the technology that the prior art cannot accurately position the pupil center. problem.

It should be noted that before the preset model based on the semi-supervised learning acquires the binary image corresponding to the preset area, the preset model needs to be constructed, and the specific steps are as follows:

Step S10, acquiring a first type of training set and a second type of training set, wherein the first type of training set and the second type of training set each include one or more images to be trained;

Step S12, acquiring a network model, wherein the network model is used to convert a plurality of images to be trained from an original image to a binary image;

Step S14, constructing a loss function of the network model;

Step S16, constructing a preset model according to the loss function of the first type training set, the second type training set, and the network model.

It should be noted that a plurality of images to be trained constitute a to-be-trained image set, and the to-be-trained image set includes a first type training set and a second type training set, wherein the first type training set is an unlabeled training set, that is, the original There is no correspondence between the image and the binary image, as shown in Figure 3 (a), an optional set of unlabeled training sets; the second type of training set is a labeled training set, that is, the original image and the binary image One-to-one correspondence, as shown in Figure 3(b), is an illustration of an optional tagged training set. Here, in FIGS. 3(a) and 3(b), x represents an original image, and y represents a binary image.

In addition, it should be noted that the foregoing network model is a Generative Adversarial Networks (GAN), which may include two GAN networks, wherein one GAN network is used to convert an image from an original image to two. The value image, while another GAN network is used to convert the binary image to the original image. After obtaining the above network model, the loss function of the network model can be constructed based on the network model, and the specific steps are as follows:

Step S140, obtaining a hyperparameter of the network model;

Step S142, in the case that the network model performs unsupervised learning, determining, according to the hyperparameter, the loss function of the network model is the first loss function and the second loss function;

Step S144, in the case that the network model performs supervised learning, the loss function of the network model is determined based on the hyperparameter as the third loss function and the fourth loss function.

It should be noted that the hyperparameter of the network model refers to a parameter that sets a value before starting the learning process in the context of machine learning. In the present application, the hyperparameters of the network model include at least one or more of the following: learning rate, ratio of unsupervised learning to supervised learning, number of batch images, and number of training rounds for training the network model.

Optionally, when unsupervised learning is applied to the network model, the first loss function is a loss function of the generator, and the second loss function is a loss function of the discriminator, wherein the first loss function is:

l _g =λ _Y ||yG _A (G _B (y))||+λ _X ||xG _B (G _A (x))||-D _B (G _B (y))-D _A (G _A (x))

The second loss function is:

l _DA = D _A (G _A (x)) - D _A (y)

l _DB = D _B (G _B (x)) - D _B (x)

When supervised learning is applied to the network model, the third loss function is the loss function of the generator, and the fourth loss function is the loss function of the discriminator, wherein the fourth loss function is the same as the second loss function, ie, supervised learning In the case of unsupervised learning, the way the discriminator is updated is unchanged. Thus, the third loss function is:

l _g =λ _Y ||yG _A (x)||+λ _X ||xG _B (y)||-D _B (G _B (y))-D _A (G _A (x))

In the above formula, λ _Y and λ _X represent hyperparameters, which can be determined empirically; G _A denotes generator A, G _B denotes generator B, D _B denotes discriminator B, and D _A denotes discriminator A. X and Y respectively represent the original image domain and the binary image domain, and x and y respectively represent images of the X and Y domains.

It should be noted that, after obtaining the network model and the first type training set and the second type training set, the preset model may be constructed, that is, the loss function of the preset model is constructed, and the specific method includes the following steps:

Step S160, updating the parameters of the discriminator and the generator of the network model based on the first type training set and the second type training set, to obtain an updated network model;

Step S162: In the case that the number of updates to the network model reaches the first threshold, the preset model is constructed according to the updated network model.

Optionally, the parameters of the network model discriminator and the generator are updated based on the first type training set and the second type training set, and the updated network model includes the following steps:

Step S1602, updating parameters of the discriminator according to the second loss function based on the first type of training set;

Step S1604, updating parameters of the generator according to the first loss function based on the first type of training set;

Step S1606, in the case that the number of times of updating the parameters of the discriminator and the generator reaches the second threshold, updating the parameters of the generator according to the third loss function based on the second type of training set;

Step S1608, updating parameters of the discriminator according to the fourth loss function based on the second type of training set;

Wherein, when the number of times of updating the parameters of the discriminator and the generator reaches the third threshold, the number of updates of the network model is incremented until the number of updates of the network model reaches the first threshold.

It should be noted that the first threshold is a maximum number of updates for training the network model, and the second threshold is a parameter for updating the generator based on the unsupervised learning mode (ie, a parameter of the generator in the unsupervised learning mode) and a discriminator. The maximum number of updates of the parameters (ie, the parameters of the discriminator in the unsupervised learning mode), and the third threshold is the parameter that updates the generator based on the supervised learning mode (ie, the parameters of the generator in the supervised learning mode) and the discriminator The maximum number of updates for the parameters (that is, the parameters of the discriminator in supervised learning mode).

In an alternative embodiment, a flow chart of the construction of an optional preset model is shown in FIG. Wherein, in FIG. 4, the first threshold is n, the second threshold is n1, and the third threshold is n2. Optionally, after acquiring the training data set, that is, after acquiring the first type training set and the second type training set, initializing the parameters of the network model, specifically including initializing the weight parameter and the hyper parameter of the network model. . After the initialization process of the parameters is completed, the parameters of the generator and the parameters of the discriminator are updated by the unsupervised learning method using the unlabeled training set (ie, the first type of training set) and the gradient descent method, the parameters and discriminators in the generator. When the number of updates of the parameter reaches the second threshold (ie, n1), the update is based on the supervised learning mode, that is, the method of using the tagged training set (ie, the second type of training set) and the gradient descent is updated by the supervised learning method. The parameters of the device and the parameters of the discriminator. In the case where the parameters of the generator and the number of updates of the parameters of the discriminator reach the third threshold (ie, n2), the update of the network model is completed once, and the training of the network model is stopped. In the case where the number of times the parameters of the generator and the discriminator are updated reaches the first threshold, the preset model is constructed by the obtained generator and the discriminator at this time.

Example 2

In accordance with an embodiment of the present invention, an apparatus embodiment for determining a pupil position is also provided. The apparatus for determining a pupil position includes one or more processors, and one or more memories storing program units, wherein the program unit is executed by a processor, and the program unit includes a first acquisition module, a second acquisition module, and a third acquisition. Module and determination module. 5 is a schematic structural diagram of an apparatus for determining a pupil position according to an embodiment of the present invention. As shown in FIG. 5, the apparatus includes: a first obtaining module 501, a second acquiring module 503, a third obtaining module 505, and a determining module 507. .

The first obtaining module 501 is configured to acquire a to-be-detected image that includes a pupil; the second acquiring module 503 is configured to acquire a binary image corresponding to the preset region, where the preset region is The area of the pupil in the image to be detected is located; the third obtaining module 505 is configured to acquire the centroid of the binary image; and the determining module 507 is configured to determine the center position of the pupil according to the centroid of the binary image.

It should be noted that the first acquiring module 501, the second obtaining module 503, the third obtaining module 505, and the determining module 507 correspond to steps S102 to S108 in Embodiment 1, and the four modules are implemented by corresponding steps. The examples and application scenarios are the same, but are not limited to the contents disclosed in the above embodiment 1.

It should be noted that the foregoing first obtaining module 501, the second obtaining module 503, the third obtaining module 505, and the determining module 507 may be run in the terminal as part of the device, and the foregoing module may be executed by a processor in the terminal. The functions realized can also be terminal devices such as smart phones (such as Android phones, iOS phones, etc.), tablet computers, applause computers, and mobile Internet devices (MID), PAD, and the like.

In an optional embodiment, the device for determining a pupil position further includes: a fifth acquisition module, a sixth acquisition module, a first construction module, and a second construction module. The fifth obtaining module is configured to acquire the first type training set and the second type training set, wherein the first type training set and the second type training set each include one or more images to be trained; The trained image includes a first training image set and a second training image set; a sixth obtaining module configured to acquire a network model, wherein the network model is configured to convert the plurality of images to be trained from the original image to the binary image; A building block is configured to construct a loss function of the network model; and a second building block is configured to construct a preset model according to the loss function of the first type training set, the second type training set, and the network model.

It should be noted that the foregoing fifth obtaining module, the sixth obtaining module, the first building module, and the second building module correspond to steps S10 to S16 in Embodiment 1, the examples implemented by the four modules and the corresponding steps, and The application scenario is the same, but is not limited to the content disclosed in the above embodiment 1.

It should be noted that the foregoing fifth obtaining module, the sixth obtaining module, the first building module, and the second building module may be run in the terminal as part of the device, and may be implemented by using the processor in the terminal. Features.

In an optional embodiment, the first building module includes: a seventh acquiring module, a first determining module, and a second determining module. The seventh obtaining module is configured to acquire a hyperparameter of the network model; the first determining module is configured to determine, according to the hyperparameter, the loss function of the network model as the first loss function and the first parameter in the case that the network model performs unsupervised learning The second loss function is configured to determine that the loss function of the network model is a third loss function and a fourth loss function based on the hyperparameter in the case that the network model performs supervised learning.

It should be noted that the foregoing seventh obtaining module, the first determining module, and the second determining module correspond to steps S140 to S144 in Embodiment 1, and the three modules are the same as the examples and application scenarios implemented by the corresponding steps, but It is not limited to the contents disclosed in the above embodiment 1.

It should be noted that the seventh obtaining module, the first determining module, and the second determining module may be run in the terminal as part of the device, and the functions implemented by the module may be performed by a processor in the terminal.

In an optional embodiment, the second building module includes: a first update module and a third building module. The first update module is configured to update the parameters of the network model discriminator and the generator based on the first type training set and the second type training set to obtain an updated network model; and the third building module is set to In the case that the number of updates to the network model reaches the first threshold, the preset model is constructed according to the updated network model.

It should be noted that the foregoing first update module and the third construction module correspond to step S160 to step S162 in the first embodiment, and the two modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the above embodiments. 1 published content.

It should be noted that the first update module and the third build module may be run in the terminal as part of the device, and the functions implemented by the module may be performed by a processor in the terminal.

In an optional embodiment, the first update module includes: a second update module, a third update module, a fourth update module, and a fifth update module. The second update module is configured to update the parameter of the discriminator according to the second loss function based on the first type of training set; the third update module is configured to update the parameter of the generator according to the first loss function based on the first type of training set; a fourth updating module, configured to update a parameter of the generator according to the third loss function based on the second type of training set if the number of times of updating the parameters of the discriminator and the generator reaches a second threshold; the fifth update module, And being configured to update the parameter of the discriminator according to the fourth loss function based on the second type of training set; wherein, in the case that the number of times the parameter of the discriminator and the generator is updated reaches a third threshold, the number of times of updating the network model is performed Add one operation until the number of updates of the network model reaches the first threshold.

It should be noted that the foregoing second update module, third update module, fourth update module, and fifth update module correspond to steps S1602 to S1608 in Embodiment 1, and examples implemented by four modules and corresponding steps are The application scenario is the same, but is not limited to the content disclosed in the above embodiment 1.

It should be noted that the foregoing second update module, third update module, fourth update module, and fifth update module may be run in the terminal as part of the device, and may be implemented by using the processor in the terminal. Features.

Example 3

According to another aspect of an embodiment of the present invention, there is further provided a storage medium comprising a stored program, wherein the program executes the method of determining a pupil position in Embodiment 1.

The various functional modules provided by the embodiments of the present application may be operated in a device or a similar computing device that determines the position of the pupil, or may be stored as part of the storage medium.

Optionally, in this embodiment, a computer program is stored in the storage medium, wherein the computer program is configured to be executed to execute a data processing method.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: acquiring an image to be detected including the pupil; acquiring a binary image corresponding to the preset region based on the preset model of semi-supervised learning The preset area is the area where the pupil is located in the image to be detected; the centroid of the binary image is obtained; and the center position of the pupil is determined according to the centroid of the binary image.

Alternatively, in the present embodiment, the storage medium may also be provided as program code for various preferred or optional method steps provided by the method of determining the pupil position.

Example 4

According to another aspect of an embodiment of the present invention, there is further provided a processor for executing a program, wherein the method of determining a pupil position in Embodiment 1 is executed while the program is running.

In the present embodiment of the invention, the processor can determine the running procedure of the method of the pupil position.

Optionally, in this embodiment, the processor may be configured to: perform: acquiring an image to be detected that includes the pupil; and acquiring a binary image corresponding to the preset region based on the preset model of the semi-supervised learning, where Let the area be the area where the pupil is located in the image to be detected; obtain the centroid of the binary image; determine the center position of the pupil according to the centroid of the binary image.

The above processor can execute various functional applications and data processing by running software programs and modules stored in the memory, that is, implementing the above-described method of determining the pupil position.

One of ordinary skill in the art will appreciate that all or part of the various steps of the above-described embodiments can be accomplished by a program-independent device-independent hardware that determines the position of the pupil, which can be stored in a device that determines the position of the pupil. In the storage medium, the storage medium may include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

A method and apparatus for determining a pupil position in accordance with the present invention is described above by way of example with reference to the accompanying drawings. However, it will be understood by those skilled in the art that various modifications can be made to the method and apparatus for determining the pupil position proposed by the present invention as described above without departing from the scope of the present invention. Therefore, the scope of the invention should be determined by the content of the appended claims.

The serial numbers of the embodiments of the present invention are merely for the description, and do not represent the advantages and disadvantages of the embodiments.

In the above-mentioned embodiments of the present invention, the descriptions of the various embodiments are different, and the parts that are not detailed in a certain embodiment can be referred to the related descriptions of other embodiments.

In the several embodiments provided by the present application, it should be understood that the disclosed technical contents may be implemented in other manners. The device embodiments described above are only schematic. For example, the division of the unit may be a logical function division. In actual implementation, there may be another division manner, for example, multiple units or components may be combined or may be Integrate into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, unit or module, and may be electrical or otherwise.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may contribute to the related art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium. A number of instructions are included to cause a computer device (which may be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: a U disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and the like. .

The above description is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can also make several improvements and retouchings without departing from the principles of the present invention. It should be considered as the scope of protection of the present invention.

Claims

A method of determining the position of a pupil, comprising:

Obtaining an image to be detected containing a pupil;

Obtaining a binary image corresponding to the preset area, where the preset area is an area where the pupil is located in the image to be detected;

Obtaining a centroid of the binary image;

A center position of the pupil is determined according to a centroid of the binary image.
The method according to claim 1, wherein before the obtaining a binary image corresponding to the preset area, the method further comprises:

Obtaining a first type of training set and a second type of training set, wherein the first type of training set and the second type of training set each include one or more images to be trained;

Obtaining a network model, wherein the network model is configured to convert the plurality of images to be trained from an original image to the binary image;

Constructing a loss function of the network model;

The preset model is constructed according to the first type training set, the second type training set, and the loss function of the network model.
The method of claim 2 wherein constructing the loss function of the network model comprises:

Obtaining a hyperparameter of the network model;

And determining, in the case that the network model performs unsupervised learning, a loss function of the network model based on the hyperparameter as a first loss function and a second loss function;

In the case where the network model performs supervised learning, the loss function of the network model is determined to be a third loss function and a fourth loss function based on the hyperparameter.
The method of claim 3, wherein constructing the preset model according to the loss function of the first type of training set, the second type of training set, and the network model comprises:

Updating parameters of the discriminator and the generator of the network model based on the first type of training set and the second type of training set to obtain an updated network model;

In a case where the number of updates to the network model reaches a first threshold, the preset model is constructed according to the updated network model.
The method according to claim 4, wherein the parameters of the discriminator and the generator of the network model are updated based on the first type of training set and the second type of training set, and the updated network model is obtained. :

Updating parameters of the discriminator based on the second loss function based on the first type of training set;

Updating parameters of the generator based on the first loss function based on the first type of training set;

And in a case that the number of times the parameter of the discriminator and the generator is updated reaches a second threshold, the parameter of the generator is updated according to the third loss function based on the second type of training set;

Updating parameters of the discriminator based on the fourth loss function based on the second type of training set;

Wherein, when the number of times the parameter of the discriminator and the generator is updated reaches a third threshold, the number of updates of the network model is increased until the number of updates of the network model reaches The first threshold is described.
An apparatus for determining a pupil position, comprising one or more processors, and one or more memories storing program units, wherein the program units are executed by the processor, the program units comprising:

a first acquiring module, configured to acquire an image to be detected including a pupil;

The second obtaining module is configured to acquire a binary image corresponding to the preset area according to the preset model of the semi-supervised learning, wherein the preset area is an area where the pupil is located in the image to be detected;

a third obtaining module, configured to acquire a centroid of the binary image;

And a determining module configured to determine a center position of the pupil according to a centroid of the binary image.
The apparatus of claim 6 wherein said apparatus further comprises:

a fifth acquiring module, configured to acquire a first type of training set and a second type of training set, wherein the first type of training set and the second type of training set each include one or more images to be trained;

a sixth obtaining module, configured to acquire a network model, wherein the network model is configured to convert the plurality of images to be trained from an original image to the binary image;

a first building block, configured to construct a loss function of the network model;

The second building module is configured to construct the preset model according to the first type training set, the second type training set, and the loss function of the network model.
The apparatus of claim 7, wherein the first building block comprises:

a seventh obtaining module, configured to acquire a hyperparameter of the network model;

a first determining module, configured to determine, according to the hyperparameter, a loss function of the network model as a first loss function and a second loss function, in the case that the network model performs unsupervised learning;

And a second determining module, configured to determine, according to the hyperparameter, a loss function of the network model as a third loss function and a fourth loss function, if the network model performs supervised learning.
The apparatus of claim 8 wherein said second building block comprises:

a first update module, configured to update parameters of the discriminator and the generator of the network model based on the first type of training set and the second type of training set to obtain an updated network model;

The third building module is configured to construct the preset model according to the updated network model if the number of updates to the network model reaches a first threshold.
The apparatus of claim 9, wherein the first update module comprises:

a second update module, configured to update parameters of the discriminator according to the second loss function based on the first type of training set;

a third update module, configured to update parameters of the generator according to the first loss function based on the first type of training set;

a fourth update module, configured to update, according to the third type of training set, the third loss function, based on the number of times the parameter of the discriminator and the generator is updated to reach a second threshold The parameters of the generator;

a fifth update module, configured to update parameters of the discriminator according to the fourth loss function based on the second type of training set;

Wherein, when the number of times the parameter of the discriminator and the generator is updated reaches a third threshold, the number of updates of the network model is increased until the number of updates of the network model reaches The first threshold is described.
A storage medium, the storage medium comprising a stored program, wherein the program performs the method of determining a pupil position according to any one of claims 1 to 5.
A processor for executing a program, wherein the program is operative to perform the method of determining a pupil position according to any one of claims 1 to 5.